Large commercial enterprises of the service sector (e.g. Banks, Telecom Operators, Utilities, Insurance companies, etc.) receive customer input (e.g. complaints) from various sources (e.g. websites, e-mails, call centres, social media, retail shops etc.). Most of these data are unstructured and their classification relies on the effort and consistency of their front-line staff, making the manual classification often inaccurate or biased.

The relevant source data, having the form of notes from customer interactions, or call recordings potentially transformed into text, or even written communications (e.g. emails received, posts and comments in social media) for customer requests, queries or complaints, contain highly valuable information, that could help the company improve the customer experience, retention and operational efficiency (including costs). Nevertheless, enforcing quality and extracting insights from this priceless data is really hard to achieve. It requires tremendous effort for post-processing, making it unrealistic at a business environment.

Current, data mining approaches tackle this problem revealing a significant part of the insight hidden in such sources. However spelling errors, language variations, voice to text inefficiencies, and many other expected discrepancies make it had for this technique to achieve accuracy beyond certain limits. Moreover, the processing time increases proportionally or sometimes exponentially to the volumes of data, making this approach not ideal for large data volumes or real – time applications.

Incelligent provides a game - changer method, that of addressing this class of problems using a proprietary machine learning - based natural language processing technique. No matter what the objective is (e.g., correlate product or customer segment with types of complaints or queries, identify trends, predict dynamics, analyze what – if scenarios, etc.) Incelligent’s platform automates the process, therefore, achieving a more accurate and objective text categorization, resulting in much more efficient management of natural language - rich customer input data.

This achievement is based on Incelligent Natural Language Processing (INLP) model, a technology leading to predictive plain text categorization, topic characterization and real-time extraction of keywords.

Incelligent technology, applied in the banking sector, has achieved an accuracy at the level of 85% - 95% even for highly-complex, multi-language, rich of jargon and misspellings input. This level of accuracy combined with the inherited adjustment to the special terminology of each industry domain and corporate culture of this method, as also its real-time component, enable smart applications including:

Smart routing of incidents to the specialized staff

Real-time escalation of the cases where the company can make a difference in alignment with each strategy

Real – time and predictive analytics

Early warning on emerging problems

Assessment of staff performance and fitness for the task allocated

Churn prediction and retention proactivity

Reduced costs for quality management at customer interaction

On the spot identification of inefficiencies of the first-line notes

Incelligent is currently provifing the INLP as well as other machine-learning-based applications for the banking, telco and other sectors, focused on proactive customer experience management and retention.